论文标题
使用深度学习技术在磁波谱图中自动识别MHD模式
Automatic Identification of MHD Modes in Magnetic Fluctuations Spectrograms using Deep Learning Techniques
论文作者
论文摘要
MHD振荡模式的控制和缓解是融合科学中的一个开放问题，因为它们可以有助于向外的粒子/能量通量，并可以将设备远离点火条件。然后，通常会以快速可靠的方式从大型实验数据库中提取模式信息的普遍兴趣。我们提出了一种基于深度学习的软件工具，该工具可以识别以Mirnov线圈频谱图作为输入数据的这些振荡模式。它使用了我们通过TJ-II Stellarator数据库手动注释的频谱图训练的卷积神经网络。我们已经测试了几个检测器架构，导致测试集的检测器AUC得分为0.99。最后，应用于在我们的频谱图中查找MHD模式，以显示如何使用该新软件工具来开采其他数据库。
The control and mitigation of MHD oscillations modes is an open problem in fusion science because they can contribute to the outward particle/energy flux and can drive the device away from ignition conditions. It is then of general interest to extract the mode information from large experimental databases in a fast and reliable way. We present a software tool based on Deep Learning that can identify these oscillations modes taking Mirnov coil spectrograms as input data. It uses Convolutional Neural Networks that we trained with manually annotated spectrograms from the TJ-II stellarator database. We have tested several detector architectures, resultingin a detector AUC score of 0.99 on the test set. Finally, it is applied to find MHD modes in our spectrograms to show how this new software tool can be used to mine other databases.
